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Regularized Discrete Optimal Transport

dc.contributor.authorFerradans, Sira
dc.contributor.authorPapadakis, Nicolas
HAL ID: 169
dc.contributor.authorRabin, Julien
HAL ID: 3181
ORCID: 0000-0003-3834-918X
dc.contributor.authorPeyré, Gabriel
HAL ID: 1211
dc.contributor.authorAujol, Jean-François
dc.date.accessioned2013-03-06T15:16:03Z
dc.date.available2013-03-06T15:16:03Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/11083
dc.descriptionLNCS n°7893
dc.language.isoenen
dc.subjectcolor transfer
dc.subjectOptimal Transport
dc.subjectvariational regularization
dc.subjectproximal splitting
dc.subjectconvex optimization
dc.subjectmanifold learning
dc.subject.ddc006.3en
dc.titleRegularized Discrete Optimal Transport
dc.typeCommunication / Conférence
dc.description.abstractenThis article introduces a generalization of discrete Optimal Transport that includes a regularity penalty and a relaxation of the bijectivity constraint. The corresponding transport plan is solved by minimizing an energy which is a convexification of an integer optimization problem. We propose to use a proximal splitting scheme to perform the minimization on large scale imaging problems. For un-regularized relaxed transport, we show that the relaxation is tight and that the transport plan is an assignment. In the general case, the regularization prevents the solution from being an assignment, but we show that the corresponding map can be used to solve imaging problems. We show an illustrative application of this discrete regularized transport to color transfer between images. This imaging problem cannot be solved in a satisfying manner without relaxing the bijective assignment constraint because of mass variation across image color palettes. Furthermore, the regularization of the transport plan helps remove colorization artifacts due to noise amplification.
dc.identifier.citationpages428-439
dc.relation.ispartoftitleScale Space and Variational Methods in Computer Vision 4th International Conference, SSVM 2013, Schloss Seggau, Leibnitz, Austria, June 2-6, 2013. Proceedings
dc.relation.ispartoftitleSSVM 2013
dc.relation.ispartofeditorArjan Kuijper, Kristian Bredies, Thomas Pock, Horst Bischof
dc.relation.ispartofpublnameSpringer
dc.relation.ispartofpublcityBerlin Heidelberg
dc.relation.ispartofdate2013
dc.relation.ispartofurl10.1007/978-3-642-38267-3
dc.identifier.urlsitehttps://hal.archives-ouvertes.fr/hal-00797078
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-3-642-38266-6
dc.relation.confcountryAUSTRIA
dc.identifier.doi10.1007/978-3-642-38267-3_36
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.date.updated2017-03-10T16:38:11Z


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